Question

A retail analytics team stores three years of daily sales transactions in BigQuery. They need to produce a 30-day demand forecast for each of 5,000 products, including confidence intervals, while keeping the data in place and minimizing custom code or infrastructure management. Which approach best meets these requirements?

Accepted Answer

Create a BigQuery ML ARIMA_PLUS model with a time-series column, item identifier, and TIME_SERIES_ID_COL to train per-product models, then use ML.FORECAST to generate predictions with prediction intervals.

Answer

Export the sales data to Cloud Storage, train a custom LSTM model on Vertex AI with a TensorFlow container, and deploy it to an endpoint for batch forecasting.

Answer

Use a BigQuery ML LINEAR_REG model with the date as a numeric feature and call ML.PREDICT to estimate future sales for each product.

Answer

Send each product's historical sales to the Vertex AI Gemini API and prompt the model to predict the next 30 days of demand.